BOL: Related items

MMseqs2: ultra fast and sensitive sequence search and clustering suite

Abhi — Wed, 06 Oct 2021 07:01:14 -0500

MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

Address of the bookmark: https://github.com/soedinglab/MMseqs2

Termal: a fast and interactive terminal-based viewer for multiple sequence alignments

LEGE — Mon, 22 Sep 2025 23:51:02 -0500

termal, a fast, interactive, terminal-based viewer for multiple sequence alignments (MSAs), designed for use on remote systems such as high-performance computing (HPC) clusters.

https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbaf208/8257678?login=true

Address of the bookmark: https://github.com/sib-swiss/termal

Arvados

Martin Jones — Sat, 20 Sep 2014 16:54:21 -0500

Arvados is a free and open source bioinformatics platform for genomic and biomedical data. User can Store | Organize | Compute | Share the data for free.

Address of the bookmark: https://arvados.org/

Randomness and Probability

Jit — Tue, 08 Nov 2016 07:17:32 -0600

Randomness and Probability

Randomness and probability are two differnet concepts: probaility is a measure (according to measure theory) which measures the randomness. Randomness is the object to be measured by probability. For example, probability is a mapping from randomness to the real number between 0 and 1. The similar examples are that the entropy measures the uncertanity; product of length and width measures the area of rectangle etc.

Please see “A mathematical theory of ability measure” by N. Kong ets for more examples to answer this question.

Bioistats Online course

Abhimanyu Singh — Thu, 10 Nov 2016 04:22:51 -0600

One of our primary focuses will be to develop an understanding of the various ways in which we can assign a probability to some chance event. We'll also learn the fundamental properties of probability, investigate how probability behaves, and learn how to calculate the probability of a new chance event.

This book is handy understanding basic concepts.

Address of the bookmark: https://onlinecourses.science.psu.edu/stat414/node/287

Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm)

Abhimanyu Singh — Thu, 29 Dec 2016 03:26:45 -0600

Prodigal (Prokaryotic Dynamic Programming Genefinding Algorithm) is a microbial (bacterial and archaeal) gene finding program developed at Oak Ridge National Laboratory and the University of Tennessee. Key features of Prodigal include:

Speed: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.
Accuracy: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).
Specificity: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.
GC-Content Indifferent: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the Pseudomonas aeruginosa curated annotations.
Metagenomic Version: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.
Ease of Use: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.
Open Source: Prodigal source code is freely available under the General Public License.

Download the latest version of Prodigal at the Prodigal github page.
or
Browse the wiki documenation.

Address of the bookmark: http://prodigal.ornl.gov/

JSON

Abhimanyu Singh — Tue, 04 Apr 2017 08:02:39 -0500

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language.

JSON is built on two structures:

A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array.
An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence.

These are universal data structures. Virtually all modern programming languages support them in one form or another. It makes sense that a data format that is interchangeable with programming languages also be based on these structures.

Address of the bookmark: http://json.org/

Tetra-Nucleotide Analysis

Jit — Thu, 04 May 2017 05:07:41 -0500

A tetra-nucleotide is a fragment of DNA sequence with 4 bases (e.g. AGTC or TTGG). Pride et al. (2003) showed that the frequency of tetra-nucleotides in bacterial genomes contain useful, albeit weak, phylogenetic signals. Even though tetra-nucleotide analysis (TNA) utilizes the information of whole genome, it is evident that it cannot replace other alignment-based phylogenetic methods such as OrthoANI or 16S rRNA phylogeny. However, TNA can be useful for phylogenetic characterization when whole genome or 16S rRNA gene information is not available. For example, a partial genomic fragment obtained from a metagenome can be identified by TNA (Teeling et al., 2004). TNA is also fast enough that it can be used as a search engine against a large genome database.

Address of the bookmark: https://chunlab.wordpress.com/tetra-nucleotide-analysis/

ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data

Jit — Mon, 19 Feb 2018 06:46:15 -0600

ETE v3, featuring numerous improvements in the underlying library of methods, and providing a novel set of standalone tools to perform common tasks in comparative genomics and phylogenetics.

The new features include

(i) building gene-based and supermatrix-based phylogenies using a single command,

(ii) testing and visualizing evolutionary models,

(iii) calculating distances between trees of different size or including duplications, and

(iv) providing seamless integration with the NCBI taxonomy database.

ETE is freely available at http://etetoolkit.org

Address of the bookmark: http://etetoolkit.org

S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences

Abhimanyu Singh — Tue, 02 Oct 2018 17:57:27 -0500

S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot